Diagnosis of Combination Faults in a Planetary Gearbox using a Modulation Signal Bispectrum based Sideband Estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme

Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

Misalignment diagnosis of a planetary gearbox based on vibration analysis

As a critical power transmission system, planetary gearbox is widely used in many industrial important machines such as wind turbines, aircraft turbine engines, helicopters. Early fault detection and diagnosis of the gearbox will help to prevent unexpected breakdowns of this important equip-ment. Misalignment is one of the major operating problems in the planetary gearbox which may be caused by inadequate system integration, variable operating conditions and differences of elastic deformations in the system. In this paper, the effect of varying degrees of installation misalignment of planetary gearbox are investigated based on vibration measurements using spectrum analysis and modulation signal bispectrum (MSB) analysis. It has shown that the misalignment can be diagnosed in the low frequency range in which the adverse effect due to co-occurrence of amplitude modula-tion and frequency modulation (AM-FM) effect is low compared with the components around meshing frequencies. Moreover, MSB produces a more accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for all operating conditions. In contrast, spectrum can produce correct results for some of the operating conditions. Keywords: Planetary gearbox, Condition Monitoring, Misalignment, Modulation signal bispectrum

A Novel Method for the Fault Diagnosis of a Planetary Gearbox based on Residual Sidebands from Modulation Signal Bispectrum Analysis

This paper presents a novel method for the fault diagnosis of planetary gearboxes based on an accurate estimation of residual sidebands using a modulation signal bispectrum (MSB). The residual sideband resulting from the out-phase superposition of vibration waves from asymmetrical multiple meshing sources are much less influenced by gear errors than that of the in-phase sidebands. Therefore, with the accurate estimation by MSB they can produce accurate and consistent diagnosis, which are evaluated by both simulating and experimental studies. However, the commonly used in-phase sidebands have high amplitudes but include gear error effects, consequently leading to poor diagnostic results

The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive

Shaft misalignments are common problems in rotating machines which cause additional dynamic and static loads, and vibrations in the system, leading to early damages and energy loss. It has been shown previously that it is possible to use motor current signature analysis to detect and diagnose this fault in motor drives. However, with a variable speed drive (VSD) system, it becomes dif-ficult to detect faults as the drive compensates for the small changes from fault ef-fects and increased noise in the measured data. In this paper, motor current signa-tures including dynamic and static data have been investigated for misalignment diagnosis in a VSD system. The study has made a systemic comparison of differ-ent control parameters between two common operation modes: open loop and sen-sorless control. Results show that fault detection features on the motor current from the sensorless mode can be the same as those of the open loop mode, however, the detection and diagnosis is significantly more difficult. In contrast, because of the additional frictional load, features from static data show results of early detection and diagnosis of different degrees of misalignment is as good as that from conventional vibration methods

Active sensor fault tolerant output feedback tracking control for wind turbine systems via T-S model

This paper presents a new approach to active sensor fault tolerant tracking control (FTTC) for offshore wind turbine (OWT) described via Takagi–Sugeno (T–S) multiple models. The FTTC strategy is designed in such way that aims to maintain nominal wind turbine controller without any change in both fault and fault-free cases. This is achieved by inserting T–S proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators to be capable to estimate different generators and rotor speed sensors fault for compensation purposes. Due to the dependency of the FTTC strategy on the fault estimation the designed observer has the capability to estimate a wide range of time varying fault signals. Moreover, the robustness of the observer against the difference between the anemometer wind speed measurement and the immeasurable effective wind speed signal has been taken into account. The corrected measurements fed to a T–S fuzzy dynamic output feedback controller (TSDOFC) designed to track the desired trajectory. The stability proof with H∞ performance and D-stability constraints is formulated as a Linear Matrix Inequality (LMI) problem. The strategy is illustrated using a non-linear benchmark system model of a wind turbine offered within a competition led by the companies Mathworks and KK-Electronic

Baseline model based structural health monitoring method under varying environment

Environment has significant impacts on the structure performance and will change features of sensor measurements on the monitored structure. The effect of varying environment needs to be considered and eliminated while conducting structural health monitoring. In order to achieve this purpose, a baseline model based structural health monitoring method is proposed in this paper. The relationship between signal features and varying environment, known as a baseline model, is first established. Then, a tolerance range of the signal feature is evaluated via a data based statistical analysis. Furthermore, the health indicator, which is defined as the proportion of signal features within the tolerance range, is used to judge whether the structural system is in normal working condition or not so as to implement the structural health monitoring. Finally, experimental data analysis for an operating wind turbine is conducted and the results demonstrate the performance of the proposed new technique

Diagnosis of faulty wind turbine bearings using tower vibration measurements †

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring

Fault diagnosis of wind turbine gearboxes through on-site measurements and vibrational signal processing

Condition monitoring of wind turbine gearboxes has attracted an impressive amount of attention in the wind energy literature. This happens for practical issues, as gearbox damages account for at least the 20% of wind turbines operational unavailability, and for scientific issues as well, because the condition monitoring of gear-based mechanical systems undergoing non-stationary operation is particularly challenging. The present work is devoted to the diagnosis of gearbox damages through a novel approach, designed exclusively for this study, based on on-site measurements and data post-processing. The main point of this method is the relatively easy repeatability, also for wind turbine practitioners, and its low impact on wind turbine operation: actually, the measuring site is not the gearbox itself, but the tower, further from the gearbox but in an easily accessible place. A real test case has been considered: a multi mega-watt wind turbine sited in Italy and owned by the Renvico company. The vibration measurements at the wind turbine suspected to be damaged and at a reference wind turbine are processed through a multivariate Novelty Detection algorithm in the feature space. The application of this algorithm is justified by univariate statistical tests on the time-domain features selected and by a visual inspection of the dataset via Principal Component Analysis. Finally, the novelty indices based on such time-domain features, computed from the accelerometric signals acquired inside the turbine tower, prove to be suitable to highlight a damaged condition in the wind-turbine gearbox, which can be then successfully monitored